initial commit

.gitignore

@@ -0,0 +1,2 @@
+.env
+*.DS_Store

Dockerfile

@@ -0,0 +1,17 @@
+# pick your favorite slim Python base
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# copy app
+COPY app.py .
+
+# expose Gradio default port
+EXPOSE 7860
+
+# launch
+CMD ["python", "app.py"]

README.md

@@ -1,11 +1,9 @@
 ---
-title: Chatfed Orchestrator
-emoji: 📊
-colorFrom: purple
-colorTo: gray
+title: ChatFed Orchestrator
+emoji: 🐠
+colorFrom: yellow
+colorTo: pink
 sdk: docker
 pinned: false
-license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app/main.py

@@ -0,0 +1,49 @@
+import gradio as gr
+from gradio_client import Client
+from langgraph import Graph, NodeFunction
+
+# node 1: call your retriever
+def retrieve_node(query: str):
+    client = Client("giz/chatfed_retriever")  # HF repo name
+    return client.predict(
+        query=query,
+        reports_filter="",
+        sources_filter="",
+        subtype_filter="",
+        year_filter="",
+        api_name="/retrieve"
+    )
+
+# node 2: call your generator
+def generate_node(query: str, context: str):
+    client = Client("giz/chatfed_generator")
+    return client.predict(
+        query=query,
+        context=context,
+        api_name="/generate"
+    )
+
+# build the graph
+graph = Graph()
+n1 = graph.add_node(NodeFunction(retrieve_node), name="retrieve")
+n2 = graph.add_node(NodeFunction(generate_node), name="generate")
+graph.link(n1, n2)
+
+# expose a simple Gradio interface that drives the graph
+def pipeline(query: str):
+    # run the graph: pass query into retrieve, then into generate
+    result = graph.run({
+        "retrieve": {"query": query}
+    })
+    return result["generate"]
+
+iface = gr.Interface(
+    fn=pipeline,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
+    outputs="text",
+    title="Modular RAG Pipeline",
+)
+
+if __name__ == "__main__":
+    # when HF spins up your container it will pick up port 7860 by default
+    iface.launch(server_name="0.0.0.0", server_port=7860)

app/utils.py

@@ -0,0 +1,46 @@
+import configparser
+import logging
+import os
+import ast
+import re
+from dotenv import load_dotenv
+
+# Local .env file
+load_dotenv()
+
+def getconfig(configfile_path: str):
+    """
+    Read the config file
+    Params
+    ----------------
+    configfile_path: file path of .cfg file
+    """
+    config = configparser.ConfigParser()
+    try:
+        config.read_file(open(configfile_path))
+        return config
+    except:
+        logging.warning("config file not found")
+
+
+def get_auth(provider: str) -> dict:
+    """Get authentication configuration for different providers"""
+    auth_configs = {
+        "huggingface": {"api_key": os.getenv("HF_TOKEN")},
+        "qdrant": {"api_key": os.getenv("QDRANT_API_KEY")},
+    }
+    
+    provider = provider.lower()  # Normalize to lowercase
+    
+    if provider not in auth_configs:
+        raise ValueError(f"Unsupported provider: {provider}")
+    
+    auth_config = auth_configs[provider]
+    api_key = auth_config.get("api_key")
+    
+    if not api_key:
+        logging.warning(f"No API key found for provider '{provider}'. Please set the appropriate environment variable.")
+        auth_config["api_key"] = None
+    
+    return auth_config
+

params.cfg

@@ -0,0 +1 @@
+

requirements.txt

@@ -0,0 +1,8 @@
+gradio[mcp]
+langchain
+langchain-community
+qdrant-client
+sentence-transformers
+gradio_client>=0.10.0
+huggingface_hub>=0.20.0
+torch